Abstract
I developed a model to estimate stand volumes using LiDAR data. Stand volumes (v) are often estimated by linear regression model using spatial volume (V), v＝aV or v＝aV＋b, where a and b as constant. This method is easy to apply, but its accuracy is known to be low. Belief of taking “a” as constant may worth for reconsideration. The concept of “Stock Ratio (s)” -defined as s＝v/V is newly proposed in this study. I focused on five representative stand characters-species, diameter of breath height, slope, height to diameter ratio and Hart-Becking index and examined the relation between these factors and Stock Ratio using 6,000 trees data obtained in field measurements. The results strongly suggested that species and Hart-Becking index have an apparent relation with Stock Ratio. Hence, it is clear that Stock Ratio will differ among species and Hart-Becking index. The relation can be modeled as s＝Bse(－As×Sr) where Sr is Hart-Becking index and, As and Bs are the constant by species. Introducing the estimation of Stock Ratio with above two factors which is proposed in this study will improve the accuracy of the estimation for the stand volume (i.e. v＝sV＝Bse(－As×Sr)V) than conventional methods.
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